{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "讲师： 沈福利\n",
    "\n",
    "本章节知识点\n",
    "\n",
    "* Matplotlib 可视化介绍\n",
    "* 导入 Matplotlib包\n",
    "* 设置风格\n",
    "* 三种绘制图的方式\n",
    "* 保持图到本地文件\n",
    "* Matplotlib的双重接口"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib 可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib是一个Python 2D绘图库，可以生成各种硬拷贝格式和跨平台交互式环境的出版物质量数据。Matplotlib可用于Python脚本，Python和IPython shell，Jupyter notebook，Web应用程序服务器和四个图形用户界面工具包。\n",
    "\n",
    "\n",
    "Matplotlib试图让简单易事的事情成为可能。你只需几行代码即可生成绘图，直方图，功率谱，条形图，误差图，散点图等。有关示例，请参阅示例图库和缩略图库。\n",
    "\n",
    "对于简单的绘图，pyplot模块提供类似MATLAB的接口，特别是与IPython结合使用时。 对于高级用户，你可以通过面向对象的界面或通过MATLAB用户熟悉的一组函数完全控制线型，字体属性，轴属性等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Matplotlib 温馨提示\n",
    "\n",
    "在我们深入讨论使用matplotlib创建可视化效果的细节之前，您应该了解一些关于使用包的有用信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入 Matplotlib包\n",
    "\n",
    "正如我们对numpy使用np简写，对pandas使用pd简写，我们将对matplotlib导入使用一些标准简写："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "“plt”接口是我们最常使用的接口，我们将在本章中看到。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看版本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpl.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设置风格\n",
    "\n",
    "\n",
    "我们将使用plt.style指令为我们的图标选择合适的美学风格。在这里，我们将设置经典样式，以确保我们创建的绘图使用经典matplotlib样式："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本节中，我们将根据需要调整此样式。请注意，从Matplotlib 1.5版开始，这里使用的样式表受到支持；如果您使用Matplotlib的早期版本，则只有默认样式可用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  如何显示你的图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib图标展示三种方式：在脚本、ipython终端或jupyter-notebook 中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 脚本方式运行\n",
    "\n",
    "如果您在脚本中使用matplotlib，函数plt.show（）就是您的朋友。show（）启动一个事件循环，查找所有当前活动的图形对象，并打开一个或多个显示图形的交互式窗口。\n",
    "\n",
    "例如： 创建一个 myplot.py 文件，然后运行\n",
    "\n",
    "```python\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x))\n",
    "\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "运行脚本，然后就会弹出一个提示框\n",
    "\n",
    "```\n",
    "$ python myplot.py\n",
    "```\n",
    "\n",
    "注意的一点是：plt.show（）命令在每个Python会话中只能使用一次，并且最常见于脚本的最后。多个show（）命令可能导致不可预测的依赖于后端的行为，应该尽量避免。\n",
    "\n",
    "运行后的效果如果：\n",
    "![avatar](images/1-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IPython shell 方式画图\n",
    "\n",
    "在ipython shell中交互使用matplotlib非常方便python）。如果指定matplotlib模式，则ipython将与matplotlib配合使用。要启用此模式，可以在启动ipython后使用%matplotlib 。\n",
    "\n",
    "```\n",
    "\n",
    "$ ipython\n",
    "\n",
    "In [3]: %matplotlib\n",
    "Using matplotlib backend: Qt5Agg\n",
    "\n",
    "In [4]: import matplotlib.pyplot as plt\n",
    "\n",
    "In [5]: import matplotlib.pyplot as plt\n",
    "   ...: import numpy as np\n",
    "   ...:\n",
    "   ...: x = np.linspace(0, 10, 100)\n",
    "   ...:\n",
    "   ...: plt.plot(x, np.sin(x))\n",
    "   ...: plt.plot(x, np.cos(x))\n",
    "   ...:\n",
    "   ...: plt.show()\n",
    "   ...:\n",
    "   ...:\n",
    "\n",
    "In [6]:\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "\n",
    "\n",
    "运行后的效果如果：\n",
    "![avatar](images/1-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IPython notebook 画图\n",
    "\n",
    "IPython jupter-notebook基于浏览器的交互式数据分析工具，它可以将代码、图形、HTML元素等组合成一个单独的可执行文档\n",
    "\n",
    "%Matplotlib inline 嵌入到jupyter-notebook的绘图静态图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行此命令后，创建绘图结果图像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.plot(x, np.sin(x), '-')\n",
    "plt.plot(x, np.cos(x), '--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 保持图到本地文件\n",
    "\n",
    "保存一张图片，通过 ``savefig()`` 命令完成。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('images/my_figure.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在当前目录下我们查看 ``my_figure.png`` 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--@ 1 zhengwenjie  staff    22K  8  3 10:57 images/my_figure.png\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh images/my_figure.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了确认它包含我们认为它包含的内容，让我们使用IPython图像对象来显示此文件的内容："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('images/my_figure.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "savefig（）中，文件格式是从给定文件名的扩展名推断出来的。通过使用Figure Canvas对象的以下方法，可以找到系统支持的文件类型列表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('images/my_figure.pdf')\n",
    "fig.savefig('images/my_figure.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ps': 'Postscript',\n",
       " 'eps': 'Encapsulated Postscript',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics',\n",
       " 'jpg': 'Joint Photographic Experts Group',\n",
       " 'jpeg': 'Joint Photographic Experts Group',\n",
       " 'tif': 'Tagged Image File Format',\n",
       " 'tiff': 'Tagged Image File Format'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.canvas.get_supported_filetypes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib的双重接口\n",
    "一个方便的matlab风格的基于状态的接口；\n",
    "一个更强大的面向对象的接口。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MATLAB风格接口\n",
    "\n",
    "Matplotlib最初是作为matlab用户的python替代品编写的，它的许多语法反映了这一事实。matlab风格的工具包含在pyplot（plt）接口中。例如，以下代码对于matlab用户来说可能非常熟悉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入绘制图\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 导入数值计算库\n",
    "import numpy as np\n",
    "\n",
    "# 生成x 轴的数据\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "plt.figure()  # create a plot figure\n",
    "\n",
    "# create the first of two panels and set current axis\n",
    "plt.subplot(2, 1, 1) # (rows, columns, panel number)\n",
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "# create the second panel and set current axis\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 面向对象的接口\n",
    "\n",
    "使用面向对象的接口，我们控制具体图表的时候更加方便"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "# First create a grid of plots\n",
    "# ax will be an array of two Axes objects\n",
    "fig, ax = plt.subplots(2)\n",
    "\n",
    "# Call plot() method on the appropriate object\n",
    "ax[0].plot(x, np.sin(x))\n",
    "ax[1].plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随着图变得更复杂，面向对象的方法可能成为一种必要"
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